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Towards Increasing Residential Market Transparency: Mapping Local Housing Prices and Dynamics

机译:增加住宅市场透明度:绘制当地房价和动态

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This article attempts to use spatial maps as a way of presenting additional information about the phenomena occurring in the housing market. In our opinion, spatial maps may facilitate understanding and provide more detailed information, which undoubtedly should increase the transparency of the housing market. The study used 12,219 transactions of apartments in Poznan in the years 2013-2017. General principles of price visualization activity and housing market dynamics were established in this study. The map of prices may reflect the location values determined by the quality of the urban infrastructure, distance from specific locations, and environmental factors. Market activity maps reveal areas where the market is dynamically developing, while information on trends in the number of transactions and price changes may demonstrate the growing or declining attractiveness of areas. The research is based on a model of hedonic regression in the form of ordinary least squares (OLS), quantile regression (QR), and geographically weighted regression (GWR). The maps presented should increase the transparency of the residential market (e.g., by providing more detailed information). However, one should bear in mind the limitations in the use of these methods resulting from a small number of transactions in a thin market.
机译:本文试图使用空间地图作为呈现有关住房市场发生的现象的其他信息的一种方式。在我们看来,空间地图可以促进理解和提供更详细的信息,无疑应该增加住房市场的透明度。该研究在2013-2017年的Poznan公寓的12,219次交易中使用了12,219次交易。这项研究成立了价格可视化活动和住房市场动态的一般原则。价格地图可以反映由城市基础设施质量,特定地点的距离和环境因素决定的位置值。市场活动地图揭示了市场动态发展的地区,而有关交易数量和价格变动的趋势的信息可能会展示区域的吸引力增长或下降。该研究基于普通最小二乘(OLS),定量回归(QR)和地理加权回归(GWR)的形式的蜂窝回归模型。所提供的地图应该提高住宅市场的透明度(例如,通过提供更详细的信息)。然而,人们应该牢记使用这些方法的局限性,这是少量交易在薄市场中的少数交易。

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